During the manufacturing of motor vehicles, certain tolerances of the components used, in particular the components of internal combustion engines or the internal combustion engines themselves, are unavoidable. Such tolerances may be, for example, differences in the compression values of the cylinders of an internal combustion engine and differences in the compression mean values between multiple internal combustion engines of the same type or series. Corresponding tolerances also occur in the installed injectors and in all exhaust-relevant components of internal combustion engines (turbocharger, air mass sensor, exhaust gas recirculation valve, etc.).
Those skilled in the art frequently refer in this context to so-called min and max components. A max injector will inject more fuel during the same activation duration than a nominal injector. A nominal injector is an injector here which corresponds to its particular specification without any deviation (i.e., has a tolerance of zero). One also refers in this case to a “golden injector” or a corresponding “golden” component. During the development of motor vehicles, a “golden system” is used in the so-called application phase. This applies in particular for the application phase of the exhaust gas optimization. A “golden system” or a “golden engine” only has nominal components.
In order to compensate for nominal value deviations in the actually constructed motor vehicles, an array of learning functions is known, to each of which specific input variables are applied. Learning functions which may be used within the scope of the present invention are, for example, provided in the case of IQA (injector quantity adjustment; compensation of the manufacturing tolerance during the injector manufacture), MCC (model-based charge control; model-supported charge regulation), PWC (pressure wave compensation; compensation of hydraulic oscillations), MBC (model-based boost pressure control; model-supported boost pressure regulation), ZFC (zero fuel calibration; correction of the pilot injection, zero-fuel quantity calibration), FBC (fuel balance control; balancing of the cylinder scattering of the injection quantity), FMA (fuel mean value adaptation; lambda-based air mass and air quantity correction), and FMO (fuel mass observer; lambda-based correction of the quantity at full load), which predominantly relate to diesel engines. Learning functions exist in the case of gasoline engines for the mixture adaptation or the torque loss adaptation, for example.
Learning functions correct engine activation parameters with the aid of corresponding correction values, for example, so that the internal combustion engine behaves like a “golden engine” after application of the correction values. Ideally, after a corresponding correction, an internal combustion engine having exclusively min or max components also has an identical power and identical exhaust gas results as an engine having nominal components. Corresponding learning functions also correct deviations or drifts which may occur during the service life of the motor vehicle. The nominal value deviations are therefore also referred to here as “manufacturing-related” and as “age-related” nominal value deviations. For example, if an injector drifts in the course of operation of a vehicle within a certain scope away from the originally provided value, this is corrected by the learning functions.
The explained learning functions have the disadvantage that they require several thousand kilometers (typically approximately 5000 km), until they may effectively act or may be activated. This is to be attributed to the fact that the learning functions must each ascertain corresponding characteristics for a variety of operating states of the motor vehicle, which are each defined differently, and are only then capable of providing the correction values. Furthermore, corresponding learning functions must be continuously recalibrated over the entire service life of the motor vehicle, which also includes the ascertainment of corresponding characteristics in multiple defined operating states.
Therefore, the need still exists for improvements in the performance of corresponding learning functions, in particular for the reduction of the time which is required for corresponding learning functions.